Development and Analysis of Geometric Descriptors for Pattern Recognition
Abstract
In Digital Image Processing and Computational Vision, descriptors are often used to extract features from images. This work uses digital image processing techniques to propose new geometric descriptors invariant to scale, rotation and revolution. They are used for the training of machine learning algorithms, which tests presented promising results that reached the correct classification of 99.58% of the study cases.
Keywords:
Digital image processing, computer vision, machine learning
References
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Breiman, L. (2001). Random forests. In Schapire, R. E., editor, Machine learning, volume 45, pages 5–32. Springer.
Carlier, A., Leonard, K., Hahmann, S., Morin, G., and Collins, M. (2016). The 2d shape structure dataset: A user annotated open access database. Computers & Graphics, 58:23–30.
Cristianini, N. and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
Deldjoo, Y., Elahi, M., Quadrana, M., and Cremonesi, P. (2018). Using visual features based on mpeg-7 and deep learning for movie recommendation. International journal of multimedia information retrieval, 7(4):207–219.
Deldjoo, Y., Quadrana, M., Elahi, M., and Cremonesi, P. (2017). Using mise-en-scnene visual features based on mpeg-7 and deep learning for movie recommendation. arXiv preprint arXiv:1704.06109.
Faceli, K., Lorena, A. C., Gama, J., and Carvalho, A. (2011). Inteligência artificial: Uma abordagem de aprendizado de máquina. Rio de Janeiro: LTC, 2:192.
Gonzalez, R. C. and Woods, R. E. (2007). Digital Image Processing. Ed III. Pearson Education Asia, New Delhi.
Haykin, S. (2007). Redes neurais: princípios e pr´atica. Ed. II. Bookman Editora, São Paulo
Khalid, S., Sabir, B., Jabbar, S., and Chilamkurti, N. (2017). Precise shape matching of large shape datasets using hybrid approach. Journal of Parallel and Distributed Computing, 110:16–30.
Kohavi, R. et al. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai, volume 14, pages 1137–1145. Stanford, CA.
Kov´acs, Z. L. (2002). Redes neurais artificiais: Fundamentos e aplicação. Ed. IV. Editora Livraria da Fisica, São Paulo.
Le Cessie, S. and Van Houwelingen, J. C. (1992). Ridge estimators in logistic regression. Applied statistics, pages 191–201.
Majidpour, J., Khezri, E., Hassanzade, H., and Mohammed, K. S. (2015). Interactive tool to improve the automatic image annotation using mpeg-7 and multi-class svm. In 2015 7th Conference on Information and Knowledge Technology (IKT), pages 1–7.
Sampaio, W. B. (2015). Detecc¸ ˜ao de massas em imagens mamogr´aficas usando uma metodologia adaptada `a densidade da mama. Tese de doutorado - programa de p´osgraduação em engenharia de eletricidade, PPGEE, Universidade Federal do Maranh˜ao.
Toussaint, G. (1974). Bibliography on estimation of misclassification. IEEE Transactions on information Theory, 20(4):472–479.
Published
2019-09-25
How to Cite
DO CARMO, Paulo N. S.; DE SAMPAIO, Wener B..
Development and Analysis of Geometric Descriptors for Pattern Recognition. In: REGIONAL SCHOOL ON COMPUTING OF CEARÁ, MARANHÃO, AND PIAUÍ (ERCEMAPI), 7. , 2019, São Luís/MA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 79-86.
